LLAMA：利用学习自动管理算法
LLAMA: Leveraging Learning to Automatically Manage Algorithms
摘要：
算法组合和选择方法已经取得了显着的改善，比单一的求解器。然而，这种系统的实施往往是高度定制和特定的问题域。这使得研究人员很难为他们的具体问题探索不同的技术。我们提出LLAMA，一个模块化和可扩展的工具包实现为一个R包，便于探索一系列不同的投资组合技术的任何问题域。它实现了文献中最常用的算法选择方法，并利用了R中广泛的机器学习算法和技术库。我们描述了目前的能力和限制的工具包，并说明其使用的一组例子SAT问题。
英文摘要：
Algorithm portfolio and selection approaches have achieved remarkable improvements over single solvers. However, the implementation of such systems is often highly customised and specific to the problem domain. This makes it difficult for researchers to explore different techniques for their specific problems. We present LLAMA, a modular and extensible toolkit implemented as an R package that facilitates the exploration of a range of different portfolio techniques on any problem domain. It implements the algorithm selection approaches most commonly used in the literature and leverages the extensive library of machine learning algorithms and techniques in R. We describe the current capabilities and limitations of the toolkit and illustrate its usage on a set of example SAT problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
